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B-TECH in Computational And Data Science at National Institute of Technology Karnataka, Surathkal
Dakshina Kannada, Karnataka
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About the Specialization
What is Computational and Data Science at National Institute of Technology Karnataka, Surathkal Dakshina Kannada?
This Computational and Data Science (CDS) program at NITK, Mangaluru, focuses on equipping students with advanced skills in data analysis, machine learning, and computational techniques. Tailored to meet the escalating demand for data scientists in India, this program integrates core computer science principles with specialized data-centric methodologies. It prepares graduates for high-impact roles in diverse industries, addressing the complex challenges of modern data-driven enterprises.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming seeking entry into the booming data science and AI fields. It also benefits working professionals looking to upskill in cutting-edge data technologies, and career changers transitioning into analytical roles. Aspiring data engineers, machine learning engineers, and data analysts will find the curriculum highly relevant.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Machine Learning Engineers, Big Data Analysts, and AI Specialists, with entry-level salaries typically ranging from INR 6-12 LPA, growing significantly with experience. The program aligns with industry needs, fostering critical thinking and problem-solving skills highly valued by Indian tech giants and startups for rapid career progression.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to mastering C/C++ and Python programming during the initial semesters. Practice regularly on coding platforms to build strong logical and problem-solving abilities crucial for all advanced data science concepts.
Tools & Resources
Hackerrank, CodeChef, LeetCode, GeeksforGeeks
Career Connection
A solid coding foundation is paramount for technical interviews and developing data-intensive applications, directly impacting placement success in top tech firms.
Build a Strong Mathematical Base- (Semester 1-3)
Focus intensely on Engineering Mathematics, Discrete Structures, and Probability and Statistics. Understand the theoretical underpinnings, as these are foundational for algorithms, machine learning, and data analytics. Attend tutorials diligently and solve extra problems.
Tools & Resources
Khan Academy, NPTEL courses, Standard textbooks
Career Connection
A robust mathematical understanding differentiates strong candidates in data science roles, enabling them to grasp complex algorithms and contribute to advanced research and development.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss difficult concepts, solve assignments collaboratively, and prepare for exams. Teaching and explaining concepts to others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
Discord/WhatsApp groups, Collaborative whiteboards
Career Connection
Develops teamwork and communication skills, vital for project-based roles and collaborative environments in the Indian IT industry, enhancing employability.
Intermediate Stage
Undertake Practical Data Projects- (Semester 3-5)
Start working on small data-related projects using tools like Python (Pandas, NumPy, Matplotlib) and SQL. Apply concepts from Data Structures, DBMS, and Data Analytics to real-world datasets, even if simple.
Tools & Resources
Kaggle datasets, Jupyter Notebooks, Google Colab, GitHub
Career Connection
Building a project portfolio demonstrates practical skills to recruiters, making you a more attractive candidate for internships and entry-level data science jobs in India.
Explore Open Source Contributions & Competitions- (Semester 4-6)
Contribute to open-source data science projects or participate in data science competitions on platforms like Kaggle. This provides exposure to diverse problems, collaboration, and learning from expert solutions.
Tools & Resources
Kaggle, GitHub, Google Summer of Code
Career Connection
Showcases initiative, problem-solving skills under pressure, and practical application of knowledge, highly valued by Indian companies hiring for innovation and rapid development.
Network and Seek Mentorship- (Semester 3-5)
Attend industry webinars, tech talks, and meetups (online or offline). Connect with alumni and professionals on platforms like LinkedIn. Seek informal mentorship to understand career paths and gain insights into industry trends in India.
Tools & Resources
LinkedIn, Industry-specific conferences (e.g., Data Science Congress)
Career Connection
Networking opens doors to internship opportunities, industry insights, and potential job referrals, significantly boosting placement prospects in a competitive Indian market.
Advanced Stage
Specialize and Certify- (Semester 6-8)
Identify a niche within data science (e.g., Deep Learning, Big Data Engineering, NLP) and delve deeper through advanced electives, online courses, and projects. Consider industry-recognized certifications to validate specialized skills.
Tools & Resources
Coursera/edX Specializations, AWS/Azure/GCP Data Certifications
Career Connection
Specialization makes you highly competitive for specific roles and often leads to higher starting salaries in Indian tech companies that require advanced expertise.
Intensive Internship and Major Project- (Semester 7-8)
Secure a rigorous internship (Semester 7) and dedicate fully to your Major Project (Semester 8). Aim for real-world impact, publishable research, or a production-ready system. These are your strongest resume builders.
Tools & Resources
University placement cell, Professor guidance, Industry partners
Career Connection
Demonstrates practical experience, problem-solving capability on complex problems, and readiness for a full-time role, crucial for securing placements in top-tier companies.
Refine Communication and Soft Skills- (Semester 6-8)
Actively work on presentation, technical writing, and communication skills through project reports, presentations, and mock interviews. Professional communication is often a deciding factor in final placements.
Tools & Resources
Toastmasters, College communication workshops, Mock interview platforms
Career Connection
Strong soft skills ensure you can articulate technical solutions effectively and collaborate in a team, making you a well-rounded professional sought after by Indian employers.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 182 Credits
Assessment: Internal: 40% for theory, 50% for practicals, External: 60% for theory, 50% for practicals
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Differential Equations |
| PH101 | Engineering Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers and Holography, Fiber Optics, Wave Optics |
| CS101 | Problem Solving and Programming | Core (Theory & Lab) | 4 | Programming Fundamentals, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures |
| HS101 | Professional Communication | Core | 3 | Communication Process, Oral Communication Skills, Written Communication, Technical Report Writing, Presentation Skills |
| ME110 | Engineering Graphics | Core (Theory & Lab) | 3 | Introduction to Engineering Graphics, Orthographic Projections, Isometric Projections, Sectional Views, Computer Aided Drafting |
| EV101 | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Social Issues and Environment, Environmental Protection Acts |
| PH102 | Physics Laboratory | Lab | 2 | Optics Experiments, Semiconductor Devices, Magnetic Field Measurements, Laser and Fiber Optics, Electrical Measurements |
| ME111 | Workshop Practice | Lab | 2 | Carpentry, Welding, Fitting, Sheet Metal, Foundry |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA102 | Engineering Mathematics – II | Core | 4 | Linear Algebra, Laplace Transforms, Fourier Series, Complex Analysis, Probability and Statistics |
| CY101 | Engineering Chemistry | Core | 4 | Electrochemistry and Batteries, Corrosion and its Control, Water Treatment, Polymers and Composites, Spectroscopy and Green Chemistry |
| EE101 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, DC and AC Machines, Power Systems and Safety |
| EC101 | Basic Electronics Engineering | Core | 4 | Semiconductor Diodes, Transistors, Rectifiers and Filters, Operational Amplifiers, Digital Logic Gates |
| HS102 | Indian Constitution and Professional Ethics | Core | 2 | Framing of Indian Constitution, Fundamental Rights and Duties, Directive Principles, Professional Ethics and Values, Cyber Ethics |
| CY102 | Chemistry Laboratory | Lab | 2 | Volumetric Analysis, Instrumental Methods, Water Analysis, Corrosion Studies, Polymer Synthesis |
| EE102 | Basic Electrical Engineering Lab | Lab | 2 | Verification of Circuit Laws, Study of CRO, DC Motor Characteristics, Transformer Tests, House Wiring |
| EC102 | Basic Electronics Engineering Lab | Lab | 2 | Diode Characteristics, Rectifiers, Transistor Amplifier, Operational Amplifier Circuits, Digital Logic Gates |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS201 | Data Structures | Core (Theory & Lab) | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Algorithms, Hashing and Sorting |
| CS202 | Discrete Mathematical Structures | Core | 4 | Logic and Proofs, Set Theory and Functions, Relations and Orderings, Graph Theory, Algebraic Structures and Combinatorics |
| CS203 | Object Oriented Programming | Core (Theory & Lab) | 4 | Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Exception Handling, Templates and Collections |
| CS204 | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, CPU Design and Instruction Sets, Memory System Design, Input/Output Organization, Pipelining and Parallelism |
| CS205 | Database Management Systems | Core (Theory & Lab) | 4 | Relational Model and SQL, ER Modeling and Normalization, Transaction Management, Concurrency Control, Database Security |
| CDS206 | Data Analytics | Core (Theory & Lab) | 4 | Introduction to Data Analytics, Data Preprocessing, Exploratory Data Analysis, Statistical Methods for Data Analysis, Introduction to Machine Learning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS251 | Design and Analysis of Algorithms | Core (Theory & Lab) | 4 | Algorithm Analysis, Sorting and Searching, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| CS252 | Operating Systems | Core (Theory & Lab) | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| CS253 | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Computability and Undecidability |
| CS254 | Computer Networks | Core (Theory & Lab) | 4 | Network Models (OSI/TCP-IP), Data Link Layer, Network Layer Protocols, Transport Layer Protocols, Application Layer Services |
| CDS255 | Foundations of Data Science | Core (Theory & Lab) | 4 | Data Science Life Cycle, Data Collection and Cleaning, Data Transformation, Exploratory Data Analysis, Statistical Inference |
| HS256 | Economics for Engineers | Core | 2 | Principles of Microeconomics, Market Structures, Macroeconomic Indicators, Financial Management, Project Evaluation Techniques |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDS301 | Artificial Intelligence | Core (Theory & Lab) | 4 | AI Agents and Search Strategies, Knowledge Representation, Machine Learning Fundamentals, Natural Language Processing, Expert Systems |
| CDS302 | Machine Learning | Core (Theory & Lab) | 4 | Supervised Learning, Unsupervised Learning, Model Evaluation and Validation, Ensemble Methods, Introduction to Deep Learning |
| CDS303 | Big Data Systems | Core (Theory & Lab) | 4 | Introduction to Big Data, Hadoop Ecosystem, Spark Framework, NoSQL Databases, Stream Processing |
| CDS304 | Optimization Techniques | Core | 4 | Linear Programming, Non-linear Programming, Integer Programming, Dynamic Programming, Heuristic Optimization |
| DE-1 | Department Elective – 1 | Elective | 3 | |
| OE-1 | Open Elective – 1 | Elective | 3 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDS351 | Deep Learning | Core (Theory & Lab) | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Transformers and Attention, Generative Models |
| CDS352 | Cloud Computing | Core (Theory & Lab) | 4 | Cloud Computing Paradigms, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Security, Cloud Deployment Models |
| CDS353 | Data Visualization | Core (Theory & Lab) | 4 | Principles of Data Visualization, Visual Encoding, Interactive Visualizations, Storytelling with Data, Visualization Tools |
| CDS354 | Research Methodology | Core | 2 | Research Problem Formulation, Research Design, Data Collection Methods, Statistical Analysis, Report Writing and Ethics |
| DE-2 | Department Elective – 2 | Elective | 4 | |
| DE-3 | Department Elective – 3 | Elective | 4 | |
| HS355 | Professional Practice, Law and Ethics | Core | 2 | Legal Systems and Contracts, Intellectual Property Rights, Cyber Law and Data Privacy, Ethical Hacking, Professional Ethics and Conduct |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDS401 | Minor Project | Project | 3 | Problem Identification, Literature Review, System Design, Implementation and Testing, Project Report and Presentation |
| CDS402 | Internship | Internship | 5 | Industry Exposure, Practical Skill Application, Problem Solving in Real-world Settings, Professional Communication, Internship Report and Viva |
| DE-4 | Department Elective – 4 | Elective | 4 | |
| DE-5 | Department Elective – 5 | Elective | 4 | |
| OE-2 | Open Elective – 2 | Elective | 3 | |
| OE-3 | Open Elective – 3 | Elective | 3 |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CDS451 | Major Project | Project | 6 | Advanced Research and Development, Complex System Design, Large-scale Implementation, Performance Evaluation, Thesis Writing and Defense |
| DE-6 | Department Elective – 6 | Elective | 4 | |
| DE-7 | Department Elective – 7 | Elective | 4 | |
| OE-4 | Open Elective – 4 | Elective | 3 |
